title: Algorithmic requirements for swarm intelligence in differently coupled collective systems.
abstract: Swarm systems are based on intermediate connectivity between individuals and dynamic neighborhoods. In natural swarms self-organizing principles bring their agents to that favorable level of connectivity. They serve as interesting sources of inspiration for control algorithms in swarm robotics on the one hand, and in modular robotics on the other hand. In this paper we demonstrate and compare a set of bio-inspired algorithms that are used to control the collective behavior of swarms and modular systems: BEECLUST, AHHS (hormone controllers), FGRN (fractal genetic regulatory networks), and VE (virtual embryogenesis). We demonstrate how such bio-inspired control paradigms bring their host systems to a level of intermediate connectivity, what delivers sufficient robustness to these systems for collective decentralized control. In parallel, these algorithms allow sufficient volatility of shared information within these systems to help preventing local optima and deadlock situations, this way keeping those systems flexible and adaptive in dynamic non-deterministic environments.
authors.affiliation: Artificial Life Laboratory at the Department of Zoology, Karl-Franzens University Graz, Universitätsplatz 2, A-8010 Graz, Austria.
authors.forename: Jürgen
authors.initials: J
authors.lastname: Stradner
authors.forename: Ronald
authors.initials: R
authors.lastname: Thenius
authors.forename: Payam
authors.initials: P
authors.lastname: Zahadat
authors.forename: Heiko
authors.initials: H
authors.lastname: Hamann
authors.forename: Karl
authors.initials: K
authors.lastname: Crailsheim
authors.forename: Thomas
authors.initials: T
authors.lastname: Schmickl